Apple today published a new entry in its online Machine Learning Journal, this time covering an on-device deep neural network for face detection, aka the technology that's used to power the facial recognition feature used in Photos and other apps.
Facial detection features were first introduced as part of iOS 10 in the Core Image framework, and it was used on-device to detect faces in photos so people could view their images by person in the Photos app.
Implementing this technology was no small feat, says Apple, as it required "orders of magnitude more memory, much more disk storage, and more computational resources."
Apple's Machine Learning Journal entry describes how Apple overcame these challenges by leveraging GPU and CPU in iOS devices, developing memory optimizations for network interference, image loading, and caching, and implementing the network in a way that did not interfere with other tasks expected on iPhone.
The new entry is well worth reading if you're interested in the specific details behind how Apple overcame these challenges to successfully implemented the feature. The technical details are dense, but understandable, and it provides some interesting insight into how facial recognition works.
With its Machine Learning Journal, Apple aims to share the complex concepts behind its technology so the users of its products can get a look behind the curtain. It also serves as a way for Apple's engineers to participate in the AI community.
Apple has previously shared several articles on Siri, including how "Hey Siri," works, and a piece on using machine learning and neural networks for refining synthetic images.
Article Link: Deep Neural Networks for Face Detection Explained on Apple's Machine Learning Journal
